We present an efficient algorithm for detecting collisions and self-collisions between highly deformable mass models, which is a combination of newly developed stochastic method and particle swarm optimization (PSO) a...
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We present an efficient algorithm for detecting collisions and self-collisions between highly deformable mass models, which is a combination of newly developed stochastic method and particle swarm optimization (PSO) algorithm. In stochastic collision detection, user can balance performance and detection quality by sampling primitive pairs within the models. To accelerate detecting process in the primitive pair space, we introduce PSO algorithm to complete the optimization for the first time. And in the end of this paper, we give the precision and efficiency evaluation about the algorithm and find it might be a reasonable choice for deformable models in collision detection
The traditional spatio-temporal database stores the quantitative data such as coordinate. But the qualitative information is more close to human thought and requires less storage space and process time. The previous q...
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The traditional spatio-temporal database stores the quantitative data such as coordinate. But the qualitative information is more close to human thought and requires less storage space and process time. The previous qualitative spatio-temporal systems were all prototype systems which did not support general spatio-temporal relation model and data input. We design the qualitative spatio-temporal database (QSTDB) based on spatio-temporal reasoning. A general spatio-temporal relation framework is put forward and applied to QSTDB. GML data can be converted to QSTDB as input. Thus QSTDB is compatible to most current spatio-temporal relation models and spatio-temporal *** can be applied to qualitative spatio-temporal query,spatio-temporal ontologies, spatio-temporal data mining and way finding systems etc..
Continuous queries are important in moving objects databases and spatio-temporal reasoning. The traditional synchronous updating algorithms of moving object pervasively focus on improving queries' execution effici...
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Continuous queries are important in moving objects databases and spatio-temporal reasoning. The traditional synchronous updating algorithms of moving object pervasively focus on improving queries' execution efficiency, while ignore the fact that the communication cost is also the bottleneck for improving query efficiency. We propose an asynchronous updating algorithm for continuous queries of moving objects. Three types of continuous range queries are discussed in the paper. Theoretical analysis and experiment results show that our algorithm substantially outperforms the traditional synchronous updating algorithms at aspects of monitoring accuracy, communication cost and CPU load balance
Moving objects databases are becoming more and more popular due to the increasing number of application domains that deal with moving entities. Continuous queries are important in moving objects databases. We summariz...
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ISBN:
(纸本)1424404754
Moving objects databases are becoming more and more popular due to the increasing number of application domains that deal with moving entities. Continuous queries are important in moving objects databases. We summarize three types of continuous queries, but only two of them have been studied before. We proposed new algorithms to process the other two types of queries. Experiment results all show that our algorithms is excellent in monitoring accuracy, communication cost and CPU load balance.
A dynamic growing neural network (DGNN) for supervised learning of pattern recognition or unsupervised learning of clustering is presented. The main ideas included in DGNN are growing, resonance, and post-prune. DGNN ...
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A dynamic growing neural network (DGNN) for supervised learning of pattern recognition or unsupervised learning of clustering is presented. The main ideas included in DGNN are growing, resonance, and post-prune. DGNN is called dynamic growing because it is based on the Hebbian learning rule and adds new neurons under certain conditions. When DGNN performs supervised learning, resonance will happen if the winner can't match the training example; this rule combines the ART/ARTMAP neural network and WTA learning rule. When DGNN performs unsupervised learning, post-prune is carried out to prevent over fitting the training data just like decision tree learning. DGNN's prune rule is based on the distance threshold. DGNN has some advantages: learning not only is stable because it grows under certain conditions; but also it is faster than back-propagation rules and favorable learned predictive accuracy in small, noisy, online or offline data sets. Three classes of simulations are performed on the primary benchmarks: circle-in-the-square and two-spirals-apart benchmarks are used to check DGNN's supervised learning and compare it with ARTMAP and BP neural networks; DGNN's unsupervised learning ability is checked on UCI Machine Learning Archive's Synthetic Control Chart Time Series data set
Continuous queries for moving objects are becoming more and more important due to the increasing number of application domains that deal with moving entities. The asynchronous updating algorithm for continuous queries...
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Continuous queries for moving objects are becoming more and more important due to the increasing number of application domains that deal with moving entities. The asynchronous updating algorithm for continuous queries of moving objects is superior to synchronous updating algorithms in communication cost. By improving Haibo Hu's rectangle safe region strategy we proposed a new continuous queries algorithm. Circle safe region and dynamic interval are adopted in our algorithm. Theory proof and experiment results show that our algorithm substantially outperforms the traditional periodic monitoring and the rectangle safe region algorithms in terms of monitoring accuracy, communication cost and CPU time. Furthermore, the mobile terminals need not have any computation ability in our algorithm.
The I/O routine in network storage has longer transferring time and overhead than local I/O. To well make use of the storage network bandwidth and meet demand of concurrent I/O requests, we need to find the appropriat...
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The I/O routine in network storage has longer transferring time and overhead than local I/O. To well make use of the storage network bandwidth and meet demand of concurrent I/O requests, we need to find the appropriate schedule policy for I/O requests at the level of the network. The focus in the paper is on how to schedule concurrent I/O data transferring in share storage network, and to improve performance such as the response time affected by scheduling method. Our objective is to analysis the response time of concurrent requests as function their schedule order such as the sequenced, parallel and hybrid policies. We then verify our results by both the analytical method and experiment
Fuzziness modeling for spatial data is currently an important problem in geographic information systems and spatial databases. In many geographical applications, spatial regions do not always have sharply defined boun...
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Fuzziness modeling for spatial data is currently an important problem in geographic information systems and spatial databases. In many geographical applications, spatial regions do not always have sharply defined boundaries but frequently their interiors and boundaries are fuzzy. A fuzzy spatial region model was proposed based on pleat set and the property of fuzzy regions was analyzed. The calculation method of relative membership of point in fuzzy region was given, and visualization model based on absolute membership value was proposed. The practical case shows that the model based on pleat set is valuable in the fields such as GIS, geography and spatial database.
This paper describes a sub-object retrieval system based on a segmentation method. We also use dynamic partial function (DPF) and indexing by locality sensitive hashing (LSH) for improving system performance. Such a s...
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This paper describes a sub-object retrieval system based on a segmentation method. We also use dynamic partial function (DPF) and indexing by locality sensitive hashing (LSH) for improving system performance. Such a system is useful for finding a sub-object from a large image database. In order to obtain the sub-object from a sample image, we use a segmentation method to cut out the object. The system utilizes the segmentation results to capture the higher-level concept of images and gets a stable and accurate result. Experimental and comparison results, which are performed using a general purpose database containing 20,000 images, are encouraging
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